How do distributed databases handle data consistency in multi-master systems?

Distributed databases in multi-master systems handle data consistency by balancing availability and correctness despite concurrent writes to multiple nodes. Since every node can accept writes, conflicts arise when updates occur simultaneously on the same data across different locations. To manage this, these systems use strategies like conflict resolution protocols, consistency models (e.g., eventual or strong consistency), and synchronization mechanisms. The goal is to ensure all nodes eventually agree on the data state without sacrificing performance or scalability. For example, a system might prioritize immediate availability with eventual consistency or enforce stricter rules to maintain strong consistency at the cost of higher latency.

One common approach is conflict detection and resolution. When two nodes update the same data, the system detects conflicts using timestamps, version vectors, or application-defined rules. For instance, last-write-wins (LWW) resolves conflicts by selecting the update with the latest timestamp, though this risks data loss if clocks are unsynchronized. More sophisticated methods, like operational transforms or Conflict-free Replicated Data Types (CRDTs), allow merges without manual intervention. CRDTs, such as increment-only counters or grow-only sets, ensure mathematically safe merges. Another strategy is synchronous replication, where a write is confirmed only after all nodes agree, but this increases latency. Alternatively, asynchronous replication allows faster writes but requires handling conflicts later, often through application logic or automated rules.

Real-world systems implement these concepts differently. Apache Cassandra uses tunable consistency, letting developers choose how many nodes must acknowledge a write (e.g., QUORUM for majority agreement) to balance speed and consistency. Amazon DynamoDB offers strongly consistent reads at the cost of higher latency or eventual consistency for faster access. CouchDB stores conflicting document versions and relies on applications to resolve them manually. PostgreSQL BDR uses logical replication with conflict hooks, allowing custom resolution logic. These examples highlight trade-offs: stricter consistency reduces availability, while relaxed models require robust conflict handling. Developers must choose strategies based on their application’s tolerance for inconsistency versus the need for real-time accuracy.